Regression trees
A Comparison of Classification/Regression Trees and Logistic Regression in Failure Models
... namelyClassification/Regression Trees (CART), against traditional logistic regression (LR) by employing a vast set of matched-pair accounts of the smallest enterprises, known as micro-entities,from ...
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evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R
... and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise ...of trees is to use global optimization methods like evolutionary ...and ...
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Prediction of Ordinal Classes Using Regression Trees
... regression trees. We will start with an algorithm for the induction of regression trees and turn it into an ordinal learner by some simple ...because regression algorithms by definition ...
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Classification and Regression Trees as a Part of Data Mining in Six Sigma Methodology
... into regression trees (the response variable is continuous) and classification trees (the response variable is quantitative discrete or qualitative – ...
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Building efficient fuzzy regression trees for large scale and high dimensional problems
... One of the main drawbacks of RTs is related to the crisp bounds of the branch condi- tions: a small change in the values of the input variables may produce an important dif- ference in the prediction. To overcome this ...
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Machine condition prognosis based on regression trees and one step ahead prediction
... ahead of time-series techniques and regression trees has been investigated. The proposed method is validated by predicting future state condition of a low methane compressor wherein the peak acceleration ...
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Factors Influencing Drug Injection History among Prisoners: A Comparison between Classification and Regression Trees and Logistic Regression Analysis
... and logistic regression models in predicting medical implications. Sensitivity, specificity, goodness of fit, coefficient of determination, sum of squared errors (difference between real and predicted values), and ...
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An Empirical study on Predicting Blood Pressure using Classification and Regression Trees
... and regression trees (CART) are proposed and applied to tackle the ...linear regression, ridge regression, the support vector machine and neural network in terms of accuracy rate, root mean ...
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Classification and regression trees for epidemiologic research: an air pollution example
... a regression tree to the residuals [15]; unfortunately, this approach is appro- priate only for Gaussian outcomes and cannot be easily applied to the residuals from generalized linear models ...ence trees, ...
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Classification and Regression Trees (CART) Theory and Applications
... in regression trees we do not have classes, but have response values, maximum tree will contain as many terminal nodes as there are observation in the dataset, because each observation has a different ...
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Semi-parametric forecasts of the implied volatility surface using regression trees
... A regression tree as starting model (regtree) in combina- tion with our boosting algorithm based on regression trees (treefgd) beats all other models that we have ...are regression ...
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TACKLING SIMPSON'S PARADOX IN BIG DATA USING CLASSIFICATION & REGRESSION TREES
... We propose a tree-based approach for detecting SP in data. Classification and regression trees are popular predictive algorithms that capture relationships between an outcome and set of inputs. They are ...
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Analisis CART (Classification and Regression Trees) Pada Faktor-Faktor
... and Regression Trees (CART) adalah salah satu metode atau algoritma dari teknik pohon ...(regression trees), sedangkan apabila variabel respon memiliki skala kategorik maka metode yang ...
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Influencing Elections with Statistics: Targeting Voters with Logistic Regression Trees
... logistic regression or standard classification tree methods like CHAID, but there is a growing interest in employing modern data min- ing ...logistic regression trees) that employs trees (with ...
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Comparing Performances of Logistic Regression, Classification & Regression Trees and Artificial Neural Networks for Predicting Albuminuria in Type 2 Diabetes Mellitus
... In this study, performances of classification methods were compared in order to predict the presence of albuminuria in type 2 diabetes mellitus patients. A retrospective analysis was performed in 266 subjects. We ...
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Robust regression trees based on M-estimators
... LS regression tree has only one terminal node, thus producing a very bad approximation of the true step func- ...The regression trees obtained from the other three split criteria all have nine ...
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Predictive Subset Selection using Regression Trees and RBF Neural Networks Hybridized with the Genetic Algorithm
... the regression trees RBF networks with regularization, the GA and I COM P(I F I M ) M isspec ...define regression tree parameters; p min is integer value of 10% of training data sample size, α ...
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A working guide to boosted regression trees
... on regression trees (including logistic regression trees), and the intuition is ...For regression problems, boosting is a form of ‘functional gradient ...first regression tree is ...
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Censoring Unbiased Regression Trees and Ensembles
... The remainder of this paper is organized as follows. Section 2.1 defines notation and data structures. Section 2.2 extends the existing theory on CUTs in a substantial way. Section 2.3 uses these results to construct ...
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Data driven approach to machine condition prognosis using least square regression trees
... Machine condition prognosis is extremely significant in foretelling the degradation of working condition and trends of fault propagation before they reach the alarm. In this study, the machine prognosis system based on ...
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